To simplify the understanding, data is typically represented graphically in all kinds of electronic as well as paper-based media. For example, charts are commonly used for presenting data on web pages, online- and print magazines. Charts depicted in print media can be digitized by capturing the chart with a camera or a scanning device for providing a digital representation of the chart. A large variety of different chart types exists, e.g. bar charts, pie charts, doughnut charts or the like. Charts of a particular type, e.g. a pie chart, may vary greatly e.g. in respect to the colors or textures used, regarding the size, type, position and orientation of labels and/or titles and regarding the interpretation of numerical separator symbols.
In case the data that is graphically represented in a digital chart image shall be used as a basis for further processing, the complexity and diversity of digital charts has hitherto often resulted in poor data quality, the extraction of erroneous data or has precluded an automated extraction of data completely. Thus, in many cases a user had to type in the data represented by a chart in a target application by hand.
Moreover, even in case the data extraction from a digital chart image was performed automatically, it often required massive user intervention and many man-machine interactions for extracting data and transferring data to a target application. For example, chart elements that were overseen by the algorithm or wrong assignments of data to series or categories had to be corrected manually.
Many of the existing programs work only on a few specific chart types or require a user to upload chart images which may comprise confidential data to remote image analysis servers.